Protein Grouping, FDR Analysis and Databases.

Transcription

1 Protein Grouping, FDR Analysis and Databases. March 15th 2012 Pratik Jagtap The Minnesota

2 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

3 Proteomics workflow Relative Abundance Relative Abundance BSATEST _ /7/2008 7:12:52 PM Trypsin RT: NL: 2.98E8 Base Peak MS BSATEST _ Protein Peptides RP HPLC MS/MS RT: Time (min) Ion Chromatogram NL: 2.34E8 Base Peak m/z= MS BSATEST _ Time (min) Mass Spectrum And Tandem Mass Spectrum Search Against Database Protein Identification

4 Proteomics workflow Orbitrap Mass spectral data. (.raw) Processing Search Algorithm Statistical validation of Protein Identification Visualization Descriptive Statistics Pathway Analysis

5 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

6 AEPTIR IDVCIVLLQHK Protein NTGDR

7 AEPTIR 85% IDVCIVLLQHK 65% Protein??% 25% NTGDR

8 AEPTIR (15%) IDVCIVLLQHK (35%) Protein (??%) (75%) NTGDR Feng J, Naiman DQ, Cooper B. Anal Chem May 15;79(10):

9 AEPTIR (15%) IDVCIVLLQHK (35%) Protein (4%) NTGDR (75%) 0.15 * 0.35 * 0.75 = 0.04 Feng J, Naiman DQ, Cooper B. Anal Chem May 15;79(10):

10 AEPTIR 85% IDVCIVLLQHK 65% Protein 96% NTGDR 25% 0.15 * 0.35 * 0.75 = 0.04 Feng J, Naiman DQ, Cooper B. Anal Chem May 15;79(10):

11 If only it were so easy

12 Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, , 2005

13 Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, , 2005

14 Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, , 2005

15 Tubulin alpha 6??% YMACCLLYR 85% Tubulin alpha 3??% Tubulin alpha 4??%

16 Tubulin alpha 6 85% 3 YMACCLLYR 85% Tubulin alpha 4 Tubulin alpha 3 85% 85% 3 3 Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75,

17 Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, , 2005

18 Tubulin alpha 6??% YMACCLLYR Tubulin alpha 3??% SIQFVDWCPTGFK Tubulin alpha 4??%

19 Basic peptide grouping scenarios Occam s Razor : When you have two competing theories that make exactly the same predictions, the simpler one is the better." Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: William of Ockham Copyright 2005 American Society for Biochemistry and Molecular Biology

20 Tubulin alpha 6 YMACCLLYR Tubulin alpha 3 SIQFVDWCPTGFK Tubulin alpha 4

21 Protein B Protein A Distinct Proteins Peptide 1 Peptide 2 100% 100% Peptide 3 Peptide 4 100% 100%

22 Protein B Protein A Indistinguishable Proteins Peptide 1 Peptide 2 Peptide 3 Peptide 4 50% 50% 50% 50% Peptide 1 Peptide 2 Peptide 3 Peptide 4 50% 50% 50% 50%

23 Protein B Protein A Differentiable Proteins Peptide 1 Peptide 2 Peptide 3 100% 50% 50% Peptide 2 Peptide 3 Peptide 4 50% 50% 100%

24 Protein B Protein A Subset Proteins Peptide 1 Peptide 2 Peptide 3 Peptide 4 100% 100% 100% 100% Peptide 2 Peptide 3 Peptide 4 0% 0% 0%

25 Protein A Protein B The Quantitative Subset Complication Peptide 1 Peptide 2 Peptide 3 Peptide 4 Peptide 2 Peptide 3 Peptide 4

26 The Similar Peptide Complication AVGNLR TRFE_HUMAN Scan Number: 2435 TLR9_HUMAN GLGNLR LRFN1_HUMAN

27 The Similar Peptide Complication AVGNLR TRFE_HUMAN Scan Number: 2435 TLR9_HUMAN LRFN1_HUMAN

28 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

29 Reverse Database Search Mass spectrum Search against database. >IPI:IPI Gene_Symbol=Tmsbl1 thymosin beta-like protein MSDKPDLSEVETFDKSKLKKTNTEEKNTLPSKETIQQEKEYNQRS >IPI:REV_IPI Gene_Symbol=Tmsbl1 thymosin beta-like protein SRQNYKEEQQITKESPLTKNEETNKKTKLKSDFTEVESLDKPDSM

30 False Discovery Rate Analysis Nature Methods - 4, (2007) Sennels et al BMC Bioinformatics 2009, 10:179 images.inmagine.com

31 # of Matches False Discovery Rate Analysis Correct Discriminant Score Slide Courtesy : Brian Searle (Proteome Software) Choi H, Ghosh D, Nesvizhskii A. J. Proteome Res :7(1):286

32 # of Matches Global / Cumulative FDR Threshold 1 : 99 % correct; 1% incorrect. Threshold 2 : 95 % correct; 5% incorrect. Threshold 3 : 90 % correct; 10% incorrect. Discriminant Score Slide Courtesy : Brian Searle (Proteome Software) Choi H, Ghosh D, Nesvizhskii A. J. Proteome Res :7(1):286

33 # of Matches Local / Instantaneous FDR Threshold 1 : 95 % correct; 5% incorrect. Threshold 2 : 60 % correct; 40% incorrect. Threshold 3 : 20 % correct; 80% incorrect. Discriminant Score Slide Courtesy : Brian Searle (Proteome Software) Choi H, Ghosh D, Nesvizhskii A. J. Proteome Res :7(1):286

34 # of Matches FDR Global Local Threshold 1 : 99 % correct; Threshold 1 : 95 % correct; 1% incorrect. 5% incorrect. Threshold 2 : 95 % correct; Threshold 2 : 60 % correct; 5% incorrect. 40% incorrect. Threshold 3 : 90 % correct; Threshold 3 : 20 % correct; 10% incorrect. 80% incorrect. Discriminant Score Slide Courtesy : Brian Searle (Proteome Software) Choi H, Ghosh D, Nesvizhskii A. J. Proteome Res :7(1):286

35 False Discovery Rate Analysis and PSPEP Report SINGLE RESULTS TABLE Sennels et al BMC Bioinformatics 2009, 10:179

36 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

37 Proteomics workflow Orbitrap Mass spectral data. (.raw) Processing Search Algorithm Statistical validation of Protein Identification Visualization Descriptive Statistics Pathway Analysis

38 Search Algorithms: Matching Spectra To Protein Sequences. Andromeda PEAKS

39 search algorithm Nature Methods - 4, (2007)

40 Protip / TINT John Chilton Mark Nelson

41 Protip Raw Data from Orbitrap msconvert mzxml format Mgf format dta format 14 node SEQUEST cluster X!TANDEM search OMSSA search SEQUEST search MASCOT search Scaffold Analysis Scaffold Viewer Powered by

42 Sequest X! tandem Mascot All Together Sequest + Mascot Sequest + X! tandem X! tandem + Mascot Combining results from multiple search algorithms increases the confidence and number of peptide and protein identifications. HUMAN DATASET

43 One hit wonders Option 1 : Throw Out One-Hit-Wonders Advantages: Easy, works! Disadvantages: Loss of sensitivity! Option 2 : Use Multiple Filters Filter 1 - Protein Mode 2 peptides/protein moderate spectrum threshold Filter 2 - Peptide Mode 1 peptide/protein high spectrum threshold Advantages: More sensitive! Disadvantages: Pretty arbitrary!

44 Uncertainty in Protein FDR Option 3 : Protein-level FDR. 12% 10% 8% 6% 4% 2% 0% Global Protein FDRs only accurate with >100 Proteins 1% Error In FDR Estimation Number of Confidently IDed Proteins

45 Local Protein FDRs Estimate the likelihood that a single protein of interest is present Are trouble at best due to stochastic sampling Shouldn t be used with <500 likely proteins

46 False Discovery Rate Analysis and PSPEP Report SINGLE RESULTS TABLE Sennels et al BMC Bioinformatics 2009, 10:179

47 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

48 Genomic and proteomic databases

49 Database Search Mass spectrum Search against database.

50 proteomic databases Choose your database according to your experimental setup GenBank allows you to submit DNA databases and not protein databases. Databases are not always correct. Sequence errors can be altered only by the submitter. Acclimatize yourself with details about your database (eg UniProt /IPI RefSeq). Transatlantic Divide : GenBank / EMBL NCBI / EBI Tranche / PRIDE NCBInr / TreMBL Searching with different databases and search algorithms and their effect on IDs. Effect of search algorithm on Protein grouping. Database size affects ranking of proteins, scoring threshold, etc. Using combined databases / Contaminant sequences.

51 Protein Grouping, FDR Analysis and Databases Overview. Protein Grouping : Concept & Methods. Fdr Analysis : Concept & Methods. Peptide and Protein identification Databases Publication Guidelines REFERENCES : Reporting Protein Identifications from MS/MS Results by Brian Searle (ProteomeSoftware Inc.) and Databases by Akhilesh Pandey (John Hopkins University) at the BioInformatics for Protein Identification workshop at Baltimore (2009).

52 Publication Standards In 2006 MCP published guidelines for reporting peptide and protein identifications Other proteomics journals have adopted similar standards Revised Paris 2 guidelines (1/1/2010).

53 Guidelines remind you to To present a complete methods/results section I. Search Parameters and Acceptance Criteria VI. Raw Data Submission Follow smart criteria for choosing results to publish II. Protein and Peptide Identification IV. Protein Inference from Peptide Assignments V. Quantification To not over-report your results III. Post-Translational Modifications

54 Software Can Make Guideline Fulfillment Easier

55 Where are the Guidelines? Molecular & Cellular Proteomics: Bradshaw, R. A., Burlingame, A. L., Carr, S., Aebersold, R., Reporting Protein Identification Data: The next Generation of Guidelines. Mol. Cell. Proteomics, 5: , Journal of Proteome Research: Beavis, R., Editorial: The Paris Consensus. J. Proteome Res., 2005, 4 (5), p 1475 Proteomics: Wilkins, M. R., Appel, R. D., Van Eyk, J. E., Maxey, C. M., et al., Guidelines for the next 10 years of proteomics. Proteomics. 2006, 6, 1, 4-8.

56 A few take home points We identify Proteins (not Peptides)! Can t stop at Peptide FDRs and Probabilities One-Hit-Wonders can be wrong and need to be seriously investigated (manually or mathematically) You can compute Protein level FDRs But take them with a grain of salt! Occam s Razor can simplify Shared Peptides Publication Standards exist to help you

57 Questions?